Figures for Paper
Anna-Leigh Brown
06/10/2020
iPSC splicing
Read in the splicing results returned by MAJIQ and make a volcano plot, only highlight genes of interest with a label.
ipsc_splicing = fread(file.path(here::here(),"data","ipsc_splicing_results.csv"))
ipsc_de = fread(file.path(here::here(),"data","ipsc_differential_expression.csv"))
splicing_dots_tables <- ipsc_splicing %>%
mutate(junction_name = case_when(gene_name %in% c("UNC13A","AGRN",
"UNC13B","PFKP","SETD5",
"ATG4B","STMN2") &
p_d_psi_0_10_per_lsv_junction > 0.9 &
deltaPSI > 0 ~ gene_name,
T ~ "")) %>%
mutate(`Novel Junction` = de_novo_junctions == 0) %>%
mutate(log10_test_stat = -log10(1 - p_d_psi_0_10_per_lsv_junction)) %>%
mutate(log10_test_stat = ifelse(is.infinite(log10_test_stat), 16, log10_test_stat)) %>%
mutate(graph_alpha = ifelse(p_d_psi_0_10_per_lsv_junction > 0.9, 1, 0.2)) %>%
mutate(label_junction = case_when(gene_name %in% c("UNC13A","AGRN",
"UNC13B","PFKP","SETD5",
"ATG4B","STMN2") &
p_d_psi_0_10_per_lsv_junction > 0.9 &
deltaPSI > 0 ~ junction_name,
T ~ ""))
fig1a = ggplot() +
geom_point(data = splicing_dots_tables %>% filter(de_novo_junctions != 0),
aes(x = deltaPSI, y =log10_test_stat,
alpha = graph_alpha,,fill = "Annotated Junction"), pch = 21, size = 10) +
geom_point(data = splicing_dots_tables %>% filter(de_novo_junctions == 0),
aes(x = deltaPSI, y =log10_test_stat,
alpha = graph_alpha,fill = "Novel Junction"), pch = 21, size = 10) +
geom_text_repel(data = splicing_dots_tables[junction_name != ""],
aes(x = deltaPSI, y =log10_test_stat,
label = label_junction,
color = as.character(de_novo_junctions)), point.padding = 0.3,
nudge_y = 0.2,
min.segment.length = 0,
box.padding = 2,
size=6,show.legend = F) +
geom_hline(yintercept = -log10(1 - .9)) +
geom_vline(xintercept = -0,linetype="dotted") +
scale_fill_manual(name = "",
breaks = c("Annotated Junction", "Novel Junction"),
values = c("Annotated Junction" = "#648FFF", "Novel Junction" = "#fe6101") ) +
scale_color_manual(name = "",
breaks = c("0", "1"),
values = c("1" = "#648FFF", "0" = "#fe6101") ) +
guides(alpha = FALSE, size = FALSE) +
theme(legend.position = 'top') +
ggpubr::theme_pubr() +
xlab("delta PSI") +
ylab(expression(paste("-Lo", g[10], " Test Statistic"))) +
theme(text = element_text(size = 24)) +
theme(legend.text=element_text(size=22)) +
xlim(-1,1)
de_table = ipsc_de %>%
mutate(contains_cryptic = gene_name %in% splicing_dots_tables[cryptic_junction == T,unique(gene_name)]) %>%
mutate(contains_cryptic = as.character(as.numeric(contains_cryptic))) %>%
mutate(label_junction = case_when(gene_name %in% c("UNC13A","AGRN",
"UNC13B","PFKP","SETD5",
"ATG4B","STMN2","TARDBP") ~ gene_name,
T ~ "")) %>%
mutate(graph_alpha = ifelse(padj < 0.1, 1, 0.2)) %>%
mutate(y_data = -log10(padj))
fig1b = ggplot(data = de_table) +
geom_point(data = de_table %>% filter(contains_cryptic == "0"),
aes(x = log2FoldChange, y = -log10(padj),
alpha = graph_alpha,fill = "No Cryptic"), pch = 21, size = 10) +
geom_point(data = de_table %>% filter(contains_cryptic == "1"),
aes(x = log2FoldChange, y = -log10(padj),
alpha = graph_alpha,fill = "Contains Cryptic"), pch = 21, size = 10)+
geom_text_repel(data = de_table[label_junction != ""],max.overlaps = 500,
aes(x = log2FoldChange,
y = -log10(padj),
label = label_junction,
color = as.character(contains_cryptic)),
nudge_y = 5,
min.segment.length = 0,
box.padding = 4,
size=6,show.legend = F) +
scale_fill_manual(name = "",
breaks = c("No Cryptic", "Contains Cryptic"),
values = c("No Cryptic" = "#648FFF", "Contains Cryptic" = "#fe6101") ) +
scale_color_manual(name = "",
breaks = c("1", "0"),
values = c("1" = "#fe6101", "0" = "#648FFF") ) +
xlim(-7.5,7.5) +
ylab(expression(paste("-Lo", g[10], " P-value"))) +
guides(alpha = FALSE, size = FALSE) +
theme(legend.position = 'top') +
ggpubr::theme_pubr() +
xlab(expression(paste("Lo", g[2], " Fold Change"))) +
theme(text = element_text(size = 24)) +
theme(legend.text=element_text(size=22)) +
geom_hline(yintercept = -log10(0.1)) +
geom_vline(xintercept=c(0), linetype="dotted")
print(fig1a)
print(fig1b)
UNC13A CE PSI across TDP-43 KD experiments
The ‘C/G’ tells which genotypes were supported by RNA-seq on rs12973192. The SK-N-DZ cell lines are het, as is the WTC11 cell line. SH-SY5Y cells are homozygote for the major allele. There was variability on the Klim hMN set on allelic expression.
rel_rna_cryptic_amount = data.table::fread(file.path(here::here(),"data","kd_experiments_relative_rna_and_unc13a_cryptic_junction_counts.csv"))
rel_rna_cryptic_amount[,cryptic_psi_full := ( UNC13A_3prime +
UNC13A_5prime + UNC13A_5prime_2 +
UNC13A_5prime_3) / (UNC13A_annotated + UNC13A_3prime +
UNC13A_5prime + UNC13A_5prime_2 +
UNC13A_5prime_3)]barplot UNC13A CE PSI - full five
rel_rna_cryptic_amount %>%
ggbarplot(,
x = "source",
add = c("mean_se","jitter"),
y = "cryptic_psi_full",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8)) +
ggpubr::theme_pubr() +
scale_fill_manual(name = "",
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(name = "",
values = c("#1C2617","#262114")
) +
ylab("UNC13A CE PSI") +
xlab("") +
guides(color = FALSE) +
scale_y_continuous(labels = scales::percent) +
theme(text = element_text(size = 20,family = 'sans'),
legend.text = element_text(size = 36,family = 'sans'),
axis.title.y = element_text(size = 28),
axis.text.y = element_text(size = 28))
barplot UNC13B NMD PSI
rel_rna_cryptic_amount %>%
ggbarplot(,
x = "source",
add = c("mean_se","jitter"),
y = "unc13b_nmd_exon_psi",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8)) +
ggpubr::theme_pubr() +
scale_fill_manual(name = "",
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(name = "",
values = c("#1C2617","#262114")
) +
ylab("UNC13B \nNMD Exon PSI") +
xlab("") +
guides(color = FALSE) +
theme(text = element_text(size = 20,family = 'sans'),
legend.text = element_text(size = 36,family = 'sans'),
axis.title.y = element_text(size = 28),
axis.text.y = element_text(size = 28))
TARDBP RNA and UNC13A Cryptic
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = TARDBP, y = cryptic_psi_full, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = TARDBP, y = cryptic_psi_full),size = 12) +
geom_smooth(aes(x = TARDBP, y = cryptic_psi_full),color = "black",method = 'lm') +
ggpubr::theme_pubr() +
theme(text = element_text(size = 20)) +
scale_x_continuous(labels = scales::percent) +
ylab("UNC13A CE PSI") +
theme(legend.title=element_blank()) +
theme(legend.position = 'bottom') `geom_smooth()` using formula 'y ~ x'
UNC13A RNA and UNC13A Cryptic
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = UNC13A, y = cryptic_psi_full, fill = source),pch = 21,size = 6) +
stat_cor(aes(x = UNC13A, y = cryptic_psi_full),size = 12,
method = 'spearman',
cor.coef.name = 'rho') +
geom_smooth(aes(x = UNC13A, y = cryptic_psi_full),color = "black",method = 'lm') +
ggpubr::theme_pubr() +
theme(text = element_text(size = 20)) +
scale_x_continuous(labels = scales::percent) +
scale_y_continuous(labels = scales::percent) +
ylab("UNC13A CE PSI") +
expand_limits(y = 1) +
theme(legend.title=element_blank()) +
theme(legend.position = 'bottom') +
theme(text = element_text(size = 18,family = 'sans'),
legend.text = element_text(size = 20,family = 'sans'),
axis.title = element_text(size = 32),
axis.text = element_text(size = 32))`geom_smooth()` using formula 'y ~ x'
## UNC13A RNA and UNC13A IR
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = UNC13A, y = normalized_unc13a_ir, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = UNC13A, y = normalized_unc13a_ir),size = 12,cor.coef.name = 'rho',method = 's') +
geom_smooth(aes(x = UNC13A, y = cryptic_psi_full),color = "black",method = 'lm') +
ggpubr::theme_pubr() +
theme(text = element_text(size = 20)) +
scale_x_continuous(labels = scales::percent) +
ylab("UNC13A Normalized IR") +
theme(legend.title=element_blank()) +
theme(legend.position = 'bottom') `geom_smooth()` using formula 'y ~ x'
## TARDBP RNA and UNC13A IR
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = TARDBP, y = normalized_unc13a_ir, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = TARDBP, y = normalized_unc13a_ir),size = 12) +
geom_smooth(aes(x = TARDBP, y = cryptic_psi_full),color = "black",method = 'lm') +
ggpubr::theme_pubr() +
theme(text = element_text(size = 20)) +
scale_x_continuous(labels = scales::percent) +
ylab("UNC13A Normalized IR") +
theme(legend.title=element_blank()) +
theme(legend.position = 'bottom') `geom_smooth()` using formula 'y ~ x'
barplot UNC13A relative RNA across experiments
rel_rna_cryptic_amount %>%
ggbarplot(,
x = "source",
add = c("mean_se","jitter"),
y = "cryptic_psi_full",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8)) +
ggpubr::theme_pubr() +
scale_fill_manual(
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(
values = c("#1C2617","#262114")
) +
ylab("UNC13A CE PSI") +
xlab("") +
guides(color = FALSE) 
stmn2 = rel_rna_cryptic_amount %>%
ggbarplot(,
x = "source",
add = c("mean_se","jitter"),
y = "stmn_2_cryptic_psi",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8)) +
ggpubr::theme_pubr() +
scale_fill_manual(
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(
values = c("#1C2617","#262114")
) +
ylab("STMN2 Cryptic PSI") +
xlab("") +
guides(color = FALSE)
####barplot UNC13B normalized IR####
unc13b_ir = rel_rna_cryptic_amount %>%
ggbarplot(,
x = "source",
add = c("mean_se","jitter"),
y = "normalized_unc13b_ir",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8)) +
ggpubr::theme_pubr() +
scale_fill_manual(
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(
values = c("#1C2617","#262114")
) +
ylab("Normalized UNC13B \nIntron Retention Ratio") +
xlab("") +
guides(color = FALSE) +
theme(legend.position = "none") +
theme(text = element_text(size = 18)) +
guides(color = FALSE) +
theme(text = element_text(size = 20,family = 'sans'),
legend.text = element_text(size = 36,family = 'sans'),
axis.title.y = element_text(size = 28),
axis.text.y = element_text(size = 28))
####barplot UNC13A normalized IR####
unc13a_ir = rel_rna_cryptic_amount %>%
ggbarplot(,
x = "source",
add = c("mean_se","jitter"),
y = "normalized_unc13a_ir",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8)) +
ggpubr::theme_pubr() +
scale_fill_manual(
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(
values = c("#1C2617","#262114")
) +
ylab("Normalized UNC13A \nIntron Retention Ratio") +
xlab("") +
theme(legend.position = "none") +
guides(color = FALSE) +
theme(text = element_text(size = 20,family = 'sans'),
legend.text = element_text(size = 36,family = 'sans'),
axis.title.y = element_text(size = 28),
axis.text.y = element_text(size = 28))
####scatterplot stmn2 normalized TARDBP####
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = TARDBP, y = stmn_2_cryptic_psi, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = TARDBP, y = stmn_2_cryptic_psi)) +
theme(legend.position = 'bottom') 
####scatterplot UNC13BIR normalized TARDBP####
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = TARDBP, y = normalized_unc13b_ir, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = TARDBP, y = normalized_unc13b_ir)) +
theme(legend.position = 'bottom')
####scatterplot UNC13AIR and UNC13A Crptic####
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = cryptic_psi_full, y = normalized_unc13a_ir, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = cryptic_psi_full, y = normalized_unc13a_ir)) +
theme(legend.position = 'bottom')
####scatterplot UNC13BIR and UNC13A Crptic####
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = cryptic_psi_full, y = normalized_unc13b_ir, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = cryptic_psi_full, y = normalized_unc13b_ir)) +
theme(legend.position = 'bottom')+
facet_wrap(~source)
scatterplot UNC13BIR and UNC13B NMD
rel_rna_cryptic_amount %>%
filter(condition != "control") %>%
ggplot() +
geom_point(aes(x = unc13b_nmd_exon_psi, y = normalized_unc13b_ir, fill = source),pch = 21,size = 4) +
stat_cor(aes(x = unc13b_nmd_exon_psi, y = normalized_unc13b_ir)) +
theme(legend.position = 'bottom')+
facet_wrap(~source)
iPSC protein abundance mass spec
peptides = fread(file.path(here::here(),"data","peptide_abdundance.csv"))
peptides %>%
mutate(gene = fct_relevel(gene, "UNC13A","UNC13B")) %>%
ggbarplot(,
x = "condition",
add = c("mean_se"),
y = "protein",
fill = 'condition',
color = 'condition',
position = position_dodge(0.8),
facet.by = 'gene') +
ggpubr::theme_pubr() +
scale_fill_manual(
values = c("#40B0A6","#E1BE6A")
) +
scale_color_manual(
values = c("#1C2617","#262114")
) +
ylab("Protein abundance") +
xlab("") +
geom_jitter(pch = 21,height = 0,aes(fill = condition),size = 2) +
guides(color = FALSE) +
facet_wrap(~gene, scales = 'free') +
theme(legend.position = 'none') +
scale_y_continuous(labels = scientific) +
stat_compare_means(comparisons = list(c("Control","TDP-43 KD")),
label = 'p.signif',tip.length = 0,
size = 10)
Patient summary statistics
clean_data_table = fread(file.path(here::here(),"data","nygc_junction_information.csv"))
clean_data_table = clean_data_table %>%
mutate(call = fct_relevel(call,
"C/C", "C/G", "G/G")) %>%
mutate(number_g_alleles = as.numeric(call) - 1) %>%
mutate(unc13a_cryptic_leaf_psi = ifelse(is.na(unc13a_cryptic_leaf_psi),0,unc13a_cryptic_leaf_psi))
print(glue::glue("Number of unique patients: {clean_data_table[,length(unique(participant_id))]}"))Number of unique patients: 377print(glue::glue("Number of unique tissue samples: {clean_data_table[,length(unique(sample))]}"))Number of unique tissue samples: 1349print("Patients Per Disease Category")[1] "Patients Per Disease Category"clean_data_table[,length(unique(participant_id)),by = disease] disease V1
1: ALS-FTD 23
2: ALS 193
3: Control 77
4: Other 11
5: FTD 61
6: ALS-AD 12print("Tissues Per Disease Category")[1] "Tissues Per Disease Category"clean_data_table[,length(unique(sample)),by = disease] disease V1
1: ALS-FTD 110
2: ALS 764
3: Control 199
4: Other 70
5: FTD 138
6: ALS-AD 68print("Number of patients per rs12973192 genotype")[1] "Number of patients per rs12973192 genotype"clean_data_table[,length(unique(participant_id)),by = call] call V1
1: C/C 166
2: G/G 58
3: C/G 153print("Number of tissues per disease")[1] "Number of tissues per disease"clean_data_table[,.N,by = c("disease","tissue_clean")] disease tissue_clean N
1: ALS-FTD Frontal_Cortex 22
2: ALS Frontal_Cortex 132
3: Control Frontal_Cortex 40
4: Other Frontal_Cortex 11
5: ALS-FTD Lumbar_Spinal_Cord 15
6: ALS Lumbar_Spinal_Cord 105
7: Control Lumbar_Spinal_Cord 33
8: Other Lumbar_Spinal_Cord 9
9: ALS-FTD Cervical_Spinal_Cord 14
10: ALS Cervical_Spinal_Cord 103
11: Control Cervical_Spinal_Cord 32
12: Other Cervical_Spinal_Cord 10
13: ALS-FTD Motor_Cortex 28
14: ALS Motor_Cortex 175
15: Control Motor_Cortex 23
16: Other Motor_Cortex 16
17: ALS-FTD Cerebellum 13
18: ALS Cerebellum 129
19: Control Cerebellum 28
20: Other Cerebellum 8
21: FTD Cerebellum 58
22: FTD Frontal_Cortex 45
23: ALS-AD Cerebellum 11
24: ALS-AD Motor_Cortex 13
25: ALS-AD Cervical_Spinal_Cord 10
26: ALS-AD Lumbar_Spinal_Cord 11
27: ALS-AD Frontal_Cortex 12
28: ALS-AD Occipital_Cortex 7
29: ALS-AD Thoracic_Spinal_Cord 4
30: ALS Occipital_Cortex 37
31: ALS Thoracic_Spinal_Cord 33
32: ALS-FTD Occipital_Cortex 6
33: Control Temporal_Cortex 23
34: ALS Temporal_Cortex 23
35: FTD Temporal_Cortex 35
36: Other Occipital_Cortex 7
37: Other Thoracic_Spinal_Cord 6
38: Control Thoracic_Spinal_Cord 5
39: Control Occipital_Cortex 5
40: ALS-FTD Thoracic_Spinal_Cord 5
41: ALS Hippocampus 27
42: ALS-FTD Hippocampus 7
43: Other Hippocampus 3
44: Control Hippocampus 10
disease tissue_clean Nprint("Number of partcipants by mutation and disease")[1] "Number of partcipants by mutation and disease"clean_data_table[,length(unique(participant_id)),by = c("mutations","disease")] mutations disease V1
1: None ALS-FTD 13
2: None ALS 145
3: C9orf72 ALS-FTD 10
4: None Control 77
5: None Other 11
6: SOD1 ALS 8
7: OPTN ALS 4
8: C9orf72 ALS 32
9: MATR3 ALS 1
10: ANG ALS 1
11: C9orf72 FTD 12
12: None ALS-AD 11
13: None FTD 42
14: C9orf72 ALS-AD 1
15: TBK1 FTD 2
16: MAPT FTD 5
17: FUS ALS 2print(glue::glue("Number of patients per pathology:"))Number of patients per pathology:clean_data_table[,length(unique(participant_id)),by = .(pathology)] pathology V1
1: ALS-FTD 23
2: ALS 193
3: control 77
4: Other 11
5: 13
6: ALS-AD 12
7: FTD-TDP-A 24
8: FTD-TDP-B 3
9: FTD-TDP-C 9
10: FTD-TAU 7
11: FTD-FUS 5UNC13A cryptic is an event that is specific to TDP-43 proteinopathy
FTLD-non-TDP are those with TAU and FUS aggregates
Non-tdp ALS are those with SOD1 or FUS mutations. The n’s are quite low on this unfortunately, only 8 ALS with SOD1 and 2 with FUS mutations.
First we look at detection rate in tissues affected by TDP-43 proteinopathy, For FTLD this is frontal and temporal Cortices, and for ALS this is lumbar, cervical, and thoracic spinal cord samples as well as motor cortex. For controls we also take all 6 tissues, frontal,temporal,motor cortices and the lumbar, cervical, and thoracic spinal cords.
(As a side note, once we do this the number of ALS-non-TDP drops down to 6 (2 FUS) because the ALS sample tissues are not balanced and not every participant has samples in every tissue)
####Inclusion reads by if TDP-potential####
boxplot_table = clean_data_table %>%
mutate(across(UNC13A_3prime_leaf:UNC13A_annotated_leaf, ~ .x / library_size,.names = "{.col}_library_norm")) %>%
filter(!tissue_clean %in% c("Choroid","Liver")) %>%
dplyr::select(sample,participant_id,mutations,disease_group2,pathology,tissue_clean,contains("_library_norm")) %>%
melt() %>%
filter(grepl("_3prime|_5prime_1",variable)) %>%
group_by(sample) %>%
mutate(inclusion_reads = sum(value)) %>%
ungroup() %>%
unique() %>%
mutate(junction_name = case_when(variable == "UNC13A_3prime_leaf_library_norm" ~ " Novel Donor",
variable == "UNC13A_5prime_1_leaf_library_norm" ~ " Short Novel Acceptor",
variable == "UNC13A_5prime_2_leaf_library_norm"~ "Long Novel Acceptor")) %>%
mutate(disease_tissue = case_when((grepl("FTLD",disease_group2) & grepl("Cortex",tissue_clean)) ~ T,
(grepl("ALS",disease_group2) & grepl("Cord|Motor",tissue_clean)) ~ T,
(grepl("Occipital",tissue_clean)) ~ F,
(grepl("Control",disease_group2) & grepl("Cord|Cortex",tissue_clean)) ~ T,
TRUE ~ F)) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean))
melt_count = clean_data_table[,.(sample,UNC13A_3prime_leaf,UNC13A_5prime_1_leaf,UNC13A_5prime_2_leaf)] %>% data.table::melt() %>% setnames(.,"value","orig_count")Pecent of patients UNC13A detected
Looking at disease tissue only, so just taking the cord and motor cortex in ALS and the frontal and temporal cortex of FTLD and then the cord and cortices in Controls.
boxplot_table %>%
filter(disease_tissue == T) %>%
mutate(detected = inclusion_reads > 0) %>%
dplyr::select(participant_id,disease_group2,detected) %>%
unique() %>%
group_by(disease_group2) %>%
mutate(n_sample = n_distinct(participant_id)) %>%
mutate(n_sample_detected = sum(detected)) %>%
dplyr::select(disease_group2,n_sample,n_sample_detected) %>%
unique() %>%
mutate(detection_rate = n_sample_detected / n_sample) %>%
mutate(disease_group2 = gsub("Control"," Control",disease_group2)) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
ggplot() +
geom_col(aes(x = detection_name, y = detection_rate)) +
ggpubr::theme_pubr() +
scale_y_continuous(lim = c(0,1),labels = scales::percent) +
ylab("Percent of Patients \n UNC13A Cryptic Detected") +
theme(text = element_text(size = 24)) +
xlab("N individuals")
Pecent of samples UNC13A detected by tissue
boxplot_table %>%
filter(!(tissue_clean %in% c("Cerebellum","Hippocampus","Occipital Cortex"))) %>%
mutate(detected = inclusion_reads > 0) %>%
dplyr::select(sample,disease_group2,detected,tissue_clean) %>%
unique() %>%
group_by(disease_group2,tissue_clean) %>%
mutate(n_sample = n_distinct(sample)) %>%
mutate(n_sample_detected = sum(detected)) %>%
dplyr::select(tissue_clean,disease_group2,n_sample,n_sample_detected) %>%
unique() %>%
mutate(detection_rate = n_sample_detected / n_sample) %>%
mutate(disease_group2 = gsub("Control"," Control",disease_group2)) %>%
ungroup() %>%
filter(grepl("ALS-TDP|FTLD-T",disease_group2)) %>%
mutate(tissue_clean = fct_relevel(tissue_clean, "Cervical Spinal Cord",
"Frontal Cortex",
"Lumbar Spinal Cord",
"Motor Cortex",
"Thoracic Spinal Cord")) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
mutate(cortex = ifelse(grepl(" Cord",tissue_clean),"cot","cor")) %>%
ggplot() +
geom_col(aes(x = detection_name, y = detection_rate,fill = detection_name),position = position_dodge2(width = 0.8, preserve = "single")) +
ggpubr::theme_pubr() +
ylab("Percent of Tissues \n UNC13A Cryptic Detected") +
theme(text = element_text(size = 18)) +
xlab("N tissues") +
facet_wrap(~tissue_clean,scales = 'free_x',nrow = 3) +
scale_y_continuous(lim = c(0,1),labels = scales::percent,expand = c(0,0) ) +
theme(axis.text.x = element_text(size = 14)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(text = element_text(size = 18)) +
scale_fill_manual(values = c("#4C97B5","#4C97B5","#4C97B5","#4C97B5","#4C97B5","red","#C87156","#C87156")) +
theme(legend.position = "none") 
Pecent of samples UNC13A detected by tissue and genotype
boxplot_table %>%
left_join(clean_data_table %>% dplyr::select(sample, call)) %>%
unique() %>%
filter(!(tissue_clean %in% c("Cerebellum","Hippocampus","Occipital Cortex"))) %>%
mutate(detected = inclusion_reads > 0) %>%
dplyr::select(sample,disease_group2,detected,tissue_clean,call) %>%
unique() %>%
group_by(disease_group2,tissue_clean,call) %>%
mutate(n_sample = n_distinct(sample)) %>%
mutate(n_sample_detected = sum(detected)) %>%
dplyr::select(tissue_clean,disease_group2,n_sample,n_sample_detected) %>%
unique() %>%
mutate(detection_rate = n_sample_detected / n_sample) %>%
mutate(disease_group2 = gsub("Control"," Control",disease_group2)) %>%
ungroup() %>%
filter(grepl("ALS-TDP|FTLD-T",disease_group2)) %>%
mutate(tissue_clean = fct_relevel(tissue_clean, "Cervical Spinal Cord",
"Frontal Cortex",
"Lumbar Spinal Cord",
"Motor Cortex",
"Thoracic Spinal Cord")) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
mutate(cortex = ifelse(grepl(" Cord",tissue_clean),"cot","cor")) %>%
ggplot() +
geom_col(aes(x = detection_name, y = detection_rate,fill = call),position = position_dodge2(width = 0.8, preserve = "single")) +
ggpubr::theme_pubr() +
ylab("Percent of Tissues \n UNC13A Cryptic Detected") +
theme(text = element_text(size = 18)) +
xlab("N tissues") +
facet_wrap(~tissue_clean,scales = 'free_x',nrow = 3) +
scale_y_continuous(lim = c(0,1),labels = scales::percent,expand = c(0,0) ) +
theme(axis.text.x = element_text(size = 14)) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(text = element_text(size = 18)) +
theme(legend.position = "bottom") Joining, by = "sample"Adding missing grouping variables: `call`
UNC13A CE level across tissue
This only plots the short novel acceptor and novel donor
boxplot_table %>%
filter(tissue_clean %in%
c("Frontal Cortex",
"Lumbar Spinal Cord",
"Cervical Spinal Cord",
"Motor Cortex",
"Temporal Cortex",
"Cerebellum") )%>%
group_by(disease_group2,tissue_clean) %>%
mutate(n_sample = n_distinct(sample)) %>%
ungroup() %>%
mutate(disease_group2 = gsub("Control", " Control",disease_group2)) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
mutate(tissue_clean = fct_relevel(tissue_clean, "Cervical Spinal Cord",
"Frontal Cortex",
"Lumbar Spinal Cord",
"Temporal Cortex",
"Motor Cortex")) %>%
ggplot(aes(x = detection_name,
y = inclusion_reads * 10^6,
fill ="red")) +
geom_boxplot(show.legend = F,position = position_dodge(preserve = "single",width = 1)) +
geom_point(size = 2,show.legend = F,pch = 21, position = position_jitterdodge(dodge.width=1,jitter.width = 0.3))+
ggplot2::facet_wrap(vars(tissue_clean),scales = "free_x",nrow = 3) +
ylab("UNC13A cryptic \n reads per million") +
theme(text = element_text(size = 12)) +
xlab("") +
scale_fill_manual(values = colorblind_pal()(4)[2:4]) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(text = element_text(size = 18)) +
theme(legend.position = "bottom") 
UNC13A CE level across tissue - split by junction type
clean_data_table %>%
mutate(across(UNC13A_3prime_leaf:UNC13A_annotated_leaf, ~ .x / library_size,.names = "{.col}_library_norm")) %>%
filter(!tissue_clean %in% c("Choroid","Liver")) %>%
dplyr::select(sample,participant_id,mutations,disease_group2,pathology,tissue_clean,contains("_library_norm")) %>%
melt() %>%
filter(grepl("_3prime|_5prime_1|_5prime_2",variable)) %>%
# filter(grepl("_5prime_2",variable)) %>%
unique() %>%
mutate(junction_name = case_when(variable == "UNC13A_3prime_leaf_library_norm" ~ " Novel Donor",
variable == "UNC13A_5prime_1_leaf_library_norm" ~ " Short Novel Acceptor",
variable == "UNC13A_5prime_2_leaf_library_norm"~ "Long Novel Acceptor")) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
dplyr::select(sample,disease_group2,
tissue_clean,
junction_name,
value) %>%
unique() %>%
filter(tissue_clean %in%
c("Frontal Cortex",
"Lumbar Spinal Cord",
"Cervical Spinal Cord",
"Motor Cortex",
"Temporal Cortex",
"Cerebellum"))%>%
group_by(disease_group2,tissue_clean) %>%
mutate(n_sample = n_distinct(sample)) %>%
ungroup() %>%
mutate(disease_group2 = gsub("Control", " Control",disease_group2)) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
mutate(tissue_clean = fct_relevel(tissue_clean, "Cervical Spinal Cord",
"Frontal Cortex",
"Lumbar Spinal Cord",
"Temporal Cortex",
"Motor Cortex")) %>%
ggplot(aes(x = detection_name,
y = value * 10^6,
fill = junction_name)) +
geom_boxplot(show.legend = F,position = position_dodge(preserve = "single")) +
geom_point(size = 2,pch = 21, alpha = 0.7, position = position_jitterdodge(jitter.width = 0.2))+
scale_y_log10() +
ggplot2::facet_wrap(vars(tissue_clean),scales = "free_x",nrow = 3) +
ylab("UNC13A cryptic \n reads per million") +
theme(text = element_text(size = 12)) +
xlab("") +
scale_fill_manual(values = colorblind_pal()(4)[2:4]) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(text = element_text(size = 18)) +
theme(legend.position = "bottom") 
UNC13A CE level across disease types
disease_comparisons = list( c("Control","ALS-TDP"),
c("Control","ALS \n non-TDP"),
c("Control","FTLD-TDP"),
c("Control","FTLD \n non-TDP" ))
table_to_test = boxplot_table %>%
filter(disease_tissue == T) %>%
group_by(disease_group2) %>%
mutate(n_sample = n_distinct(sample)) %>%
mutate(disease_group2 = gsub("Control"," Control",disease_group2)) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
dplyr::select(detection_name,
participant_id,
inclusion_reads,
disease_group2,
tissue_clean,
disease_tissue) %>%
unique()
test_pair = pairwise.wilcox.test(table_to_test$inclusion_reads, table_to_test$detection_name,
p.adjust.method = "BH") %>% broom::tidy()
test_pair = test_pair %>%
mutate(p_value_draw = case_when(p.value < 0.0001~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ paste0("Adj. p-value \n",as.character(round(p.value,2))))) %>%
mutate(y.position = seq(0.25,by = 0.1,length.out = 7))
table_to_test %>%
ggplot(aes(x = detection_name, y = inclusion_reads * 10^6)) +
geom_boxplot() +
geom_jitter(height = 0) +
scale_y_log10() +
ggpubr::theme_pubr() +
ylab("UNC13A cryptic inclusion \n reads per million") +
theme(text = element_text(size = 24)) +
xlab("N samples") +
stat_pvalue_manual(test_pair %>% filter(p.value < 0.05),
label = "p_value_draw",size = 8) +
stat_compare_means(size = 8)
UNC13A RNA expression (TPM)
UNC13A TPM by disease and tissue
comps = unique(clean_data_table$disease_group2) %>% gsub("Control", " Control",.) %>%
combn(.,2,simplify = F)
clean_data_table %>%
group_by(disease_group2,tissue_clean) %>%
mutate(n_sample = n_distinct(sample)) %>%
ungroup() %>%
mutate(disease_group2 = gsub("Control", " Control",disease_group2)) %>%
mutate(detection_name = glue::glue("{disease_group2} \n ( {n_sample} )")) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
filter(tissue_clean %in%
c("Frontal Cortex",
"Lumbar Spinal Cord",
"Cervical Spinal Cord",
"Motor Cortex",
"Temporal Cortex",
"Cerebellum") )%>%
mutate(tissue_clean = fct_relevel(tissue_clean, "Cervical Spinal Cord",
"Frontal Cortex",
"Lumbar Spinal Cord",
"Temporal Cortex",
"Motor Cortex")) %>%
ggplot(aes(x = detection_name, y = UNC13A_TPM)) +
ggpubr::theme_pubr() +
geom_boxplot(aes(fill = disease_group2)) +
geom_jitter(pch = 21, color = 'black',width = 0.2,height = 0,aes(fill = disease_group2)) +
ylab("UNC13A TPM") +
xlab("") +
guides(color = FALSE) +
theme(text = element_text(size = 20)) +
facet_wrap(~tissue_clean, scales = 'free',nrow = 3) +
theme(legend.position = 'none')
Correlation STMN2 Cryptic PSI and UNC13A TPM
only in ALS/FTLD-TDP
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
ggplot(aes(x = stmn_2_cryptic_psi_leaf, y = UNC13A_TPM)) +
geom_point() +
stat_cor(size = 8) +
geom_smooth(method = lm, se = FALSE,color = "black") +
xlab("STMN2 Cryptic PSI") +
ylab("UNC13A TPM") +
ggpubr::theme_pubr() +
theme(text = element_text(size = 18)) +
ylim(0,50)`geom_smooth()` using formula 'y ~ x'
Correlation STMN2 Cryptic PSI and UNC13A TPM - tissue separations
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
ggplot(aes(x = stmn_2_cryptic_psi_leaf, y = UNC13A_TPM)) +
geom_point() +
stat_cor(size = 8) +
geom_smooth(method = lm, se = FALSE,color = "black") +
xlab("STMN2 Cryptic PSI") +
ylab("UNC13A TPM") +
ggpubr::theme_pubr() +
theme(text = element_text(size = 18)) +
facet_wrap(~tissue_clean,scales = "free_y")`geom_smooth()` using formula 'y ~ x'
Correlation between UNC13A CE PSI and UNC13A TPM
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
filter(grepl("Cortex",tissue_clean)) %>%
filter(unc13a_cryptic_leaf_psi > 0) %>%
ggplot(aes(x = unc13a_cryptic_leaf_psi, y = UNC13B_TPM)) +
geom_point() +
stat_cor(label.y = 38,size = 8,method = 's',cor.coef.name = 'rho') +
geom_smooth(method = lm, se = FALSE,color = "black") +
ylab("UNC13A TPM") +
xlab("UNC13A CE PSI") +
ggpubr::theme_pubr() +
theme(text = element_text(size = 22)) +
scale_x_continuous(labels = scales::percent) `geom_smooth()` using formula 'y ~ x'
Correlation between UNC13A CE PSI and UNC13A TPM - tissue separated
clean_data_table %>%
filter(unc13a_cryptic_leaf_psi > 0 ) %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
ggplot(aes(x = unc13a_cryptic_leaf_psi, y = UNC13A_TPM, color = disease_group2)) +
geom_point() +
stat_cor(size = 8) +
geom_smooth(method = lm, se = FALSE,color = "black") +
ylab("UNC13A TPM") +
xlab("UNC13A CE PSI") +
ggpubr::theme_pubr() +
theme(text = element_text(size = 18)) +
facet_wrap(~tissue_clean,scales = 'free') +
scale_x_continuous(labels = scales::percent) `geom_smooth()` using formula 'y ~ x'
Relationship between STMN2 and UNC13A CE PSI in patients
####scatter plot showing the correlation in non-log space in STMN2 and cryptic PSI####
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
filter(grepl("Cortex",tissue_clean)) %>%
filter(stmn_2_cryptic_psi_leaf > 0 ) %>%
filter(unc13a_cryptic_leaf_psi > 0) %>%
ggplot(aes(x = stmn_2_cryptic_psi_leaf, y = unc13a_cryptic_leaf_psi)) +
geom_point() +
stat_cor(size = 8,method = "spearman",cor.coef.name = "rho") +
geom_smooth(method = lm, se = FALSE,color = "black") +
xlab("STMN2 Cryptic PSI") +
ylab("UNC13A CE PSI") +
ggpubr::theme_pubr() +
theme(text = element_text(size = 18)) +
scale_y_continuous(labels = scales::percent) +
scale_x_continuous(labels = scales::percent) `geom_smooth()` using formula 'y ~ x'
Effect of UNC13A genotype
UNC13A psi by genotype
genotype_comparisons = list(c("C/C", "C/G"), c("C/C", "G/G"), c("C/G", "G/G"))
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
group_by(call) %>%
mutate(n_sample = length(unique(sample))) %>%
ungroup() %>%
mutate(detection_name = glue::glue("{call} \n ( {n_sample} )")) %>%
ggplot(aes(y = unc13a_cryptic_leaf_psi, x = call, fill = call)) +
geom_boxplot() +
geom_jitter( height = 0) +
facet_wrap(~disease_group2,scales = 'free') +
ylab("UNC13A PSI") +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
stat_compare_means(comparisons = genotype_comparisons,label = "p.signif")
UNC13A psi by genotype - tissue split
genotype_comparisons = list(c("C/C", "C/G"), c("C/C", "G/G"), c("C/G", "G/G"))
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
group_by(call) %>%
mutate(n_sample = length(unique(sample))) %>%
ungroup() %>%
mutate(detection_name = glue::glue("{call} \n ( {n_sample} )")) %>%
ggplot(aes(y = unc13a_cryptic_leaf_psi, x = call, fill = call)) +
geom_boxplot() +
geom_jitter( height = 0) +
facet_wrap(~disease_group2 + tissue_clean,scales = 'free') +
ylab("UNC13A PSI") +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
stat_compare_means(comparisons = genotype_comparisons,label = "p.signif")
UNC13A psi in detected tissues by genotype - tissue split
clean_data_table %>%
filter(unc13a_cryptic_leaf_psi > 0 ) %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
group_by(call) %>%
mutate(n_sample = length(unique(sample))) %>%
ungroup() %>%
mutate(detection_name = glue::glue("{call} \n ( {n_sample} )")) %>%
ggplot(aes(y = unc13a_cryptic_leaf_psi, x = call, fill = call)) +
geom_boxplot() +
geom_jitter( height = 0) +
facet_wrap(~disease_group2 + tissue_clean,scales = 'free') +
ylab("UNC13A PSI") +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
stat_compare_means(comparisons = genotype_comparisons,label = "p.signif")
STMN2 psi by genotype
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
ggplot(aes(y = stmn_2_cryptic_psi_leaf, x = call, fill = call)) +
geom_boxplot() +
geom_jitter( height = 0) +
facet_wrap(~disease_group2,scales = 'free') +
ylab("STMN2 PSI") +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
stat_compare_means(comparisons = genotype_comparisons,label = "p.signif")
STMN2 psi by genotype - tissue split
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
ggplot(aes(y = stmn_2_cryptic_psi_leaf, x = call, fill = call)) +
geom_boxplot() +
geom_jitter( height = 0) +
facet_wrap(~disease_group2 + tissue_clean,scales = 'free') +
ylab("STMN2 PSI") +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
stat_compare_means(comparisons = genotype_comparisons,label = "p.signif")
STMN2 psi in detected tissues by genotype - tissue split
clean_data_table %>%
filter(stmn_2_cryptic_psi_leaf > 0 ) %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(tissue_clean = gsub("_"," ",tissue_clean)) %>%
ggplot(aes(y = stmn_2_cryptic_psi_leaf, x = call, fill = call)) +
geom_boxplot() +
geom_jitter( height = 0) +
facet_wrap(~disease_group2 + tissue_clean,scales = 'free') +
ylab("STMN2 PSI") +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
stat_compare_means(comparisons = genotype_comparisons,label = "p.signif")
Correlation UNC13A PSI and STMN2 PSI with genotype effect
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(grepl("Cortex",tissue_clean)) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
mutate(call = fct_relevel(call,
"C/C", "C/G", "G/G")) %>%
filter(!grepl("Cord",tissue_clean)) %>%
mutate(log10_stmn2 = log10(stmn_2_cryptic_psi_leaf)) %>%
mutate(log10_unc13a = log10(unc13a_cryptic_leaf_psi)) %>%
ggpubr::ggscatter(.,
x = "log10_stmn2",
y = "log10_unc13a",
color = 'call',
add = "reg.line", size = 3
) +
ylab("UNC13A CE PSI ") +
xlab("STMN2 Cryptic PSI ") +
stat_cor(aes( x = stmn_2_cryptic_psi_leaf,
y = unc13a_cryptic_leaf_psi,
color = call),
method = 'spearman',
cor.coef.name = 'rho',
show.legend = FALSE,
size = 14) +
scale_color_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
theme(legend.title = element_blank()) +
theme(text = element_text(size = 24),legend.text = element_text(size = 32))`geom_smooth()` using formula 'y ~ x'
Log2 Fold UNC / STMN2 CE in TDP-path with both detected
detected_comparisons = list(c("C/C \n( 26 )", "G/G \n( 21 )"),c("G/G \n( 21 )", "C/G \n( 26 )"),c("C/C \n( 26 )", "C/G \n( 26 )"))
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(grepl("Cortex",tissue_clean)) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
filter(unc13a_cryptic_leaf_psi > 0 ) %>%
filter(stmn_2_cryptic_psi_leaf > 0 ) %>%
dplyr::select(sample,tissue_clean,disease_group2,call,stmn_2_cryptic_psi_leaf,unc13a_cryptic_leaf_psi) %>%
mutate(log2_unc = log2((unc13a_cryptic_leaf_psi)/(stmn_2_cryptic_psi_leaf)))%>%
mutate(tissue_clean = gsub("_","\n",tissue_clean)) %>%
group_by(call) %>%
mutate(n_sample = length(unique(sample))) %>%
ungroup() %>%
mutate(detection_name = glue::glue("{call} \n ( {n_sample} )")) %>%
ggplot(aes(y = log2_unc, x = detection_name,fill = detection_name)) +
geom_boxplot(show.legend = F) +
geom_jitter(show.legend = F, alpha = 0.8, height = 0,pch = 21) +
stat_compare_means( label = "p.format") +
stat_compare_means(aes(group = detection_name),comparisons = detected_comparisons,label = "p.signif") +
ylab("Ratio UNC13A CE PSI / STMN2 PSI") +
ylab(expression(paste("Lo", g[2], " fold UNC13A CE PSI / STMN2 CE PSI "))) +
xlab("UNC13A rs12973192 Genotype") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 24)) +
geom_hline(yintercept = 0,size = 1.5) 
Detection rate by genotype
Barplot of detection by genotype
overall_fisher = clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
mutate(unc13a_detected = unc13a_cryptic_leaf_psi > 0) %>%
dplyr::select(participant_id,unc13a_detected,call) %>%
unique() %>%
group_by(call) %>%
mutate(n_sample = n_distinct(participant_id)) %>%
mutate(n_sample_detected = sum(unc13a_detected)) %>%
dplyr::select(call,n_sample,n_sample_detected) %>%
unique() %>%
mutate(n_non_detected = n_sample - n_sample_detected) %>%
dplyr::select(-n_sample)
over_p = overall_fisher %>%
column_to_rownames('call') %>%
chisq.test() %>%
broom::tidy() %>%
.$p.value
overall_fisher %>%
mutate(n_sample = n_non_detected + n_sample_detected) %>%
mutate(detection_rate = n_sample_detected / n_sample) %>%
mutate(detection_name = glue::glue("{call} \n ( {n_sample} )")) %>%
ggplot(aes(x = detection_name, y = detection_rate, fill = detection_name)) +
geom_col(show.legend = F) +
ggpubr::theme_pubr() +
scale_y_continuous(labels = scales::percent) +
ylab("% of TDP-43 Proteionopathy Patients \n UNC13A Cryptic Detected") +
theme(text = element_text(size = 18)) +
xlab("N individuals") +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) 
Although this difference is not significant, with the Fisher’s exact giving a p-value of 0.28.
Only looking at disease relevant tissue and in patients we see that the detection rate depends on both the level of TPD-43 pathology present, as measured by STMN2 CE expression,
detection_table = clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
mutate(call = fct_relevel(call,
"C/C", "C/G", "G/G")) %>%
mutate(stmn2_psi_groups = as.numeric(cut_interval(log10(stmn_2_cryptic_psi), n = 2))) %>%
mutate(stmn2_psi_groups = ifelse(is.na(stmn2_psi_groups)," No STMN2",stmn2_psi_groups)) %>%
mutate(stmn2_psi_groups = case_when(stmn2_psi_groups == 1 ~ " Low STMN2",
stmn2_psi_groups == 2 ~ "High STMN2",
TRUE ~ stmn2_psi_groups)) %>%
mutate(unc13a_detected = unc13a_cryptic_leaf_psi > 0) %>%
group_by(call,unc13a_detected,stmn2_psi_groups) %>%
add_count(name = "genotype_detected") %>%
dplyr::select(call,unc13a_detected,genotype_detected,stmn2_psi_groups) %>%
unique() %>%
pivot_wider(names_from = "unc13a_detected",
values_from = "genotype_detected") %>%
dplyr::rename(unc_not_detected = `FALSE`, unc_cryptic_detected = `TRUE`) %>%
mutate(total_tissue = (unc_not_detected + unc_cryptic_detected)) %>%
mutate(detection_rate = unc_cryptic_detected / total_tissue) %>%
ungroup() %>%
mutate(stmn2_psi_groups = fct_relevel(stmn2_psi_groups, "No STMN2", after = 0)) %>%
mutate(detection_name = glue::glue("{call} \n ( {total_tissue} )"))
detection_table %>%
mutate(error = sqrt((detection_rate * (1-detection_rate))/total_tissue)) %>%
ggplot(aes(x = call, y = detection_rate,fill = call)) +
geom_col(show.legend = F,position = 'dodge2') +
geom_errorbar(aes(ymin = detection_rate - error, ymax = detection_rate + error),
position = position_dodge(0.9),
width=0.2) +
facet_wrap(~stmn2_psi_groups) +
scale_fill_manual(values = c("#88CCEE","#44AA99","#1B7739")) +
scale_y_continuous(lim = c(0,0.5),labels = scales::percent) +
ylab("Percent of Samples Detected") +
ggpubr::theme_pubr() +
geom_text(aes(label = total_tissue,y = 0),vjust = 1,size = 11) +
ggpubr::theme_pubr() +
theme(text = element_text(size = 32)) +
xlab("N Tissues") 
clean_data_table %>%
filter(disease_tissue == T) %>%
filter(disease_group2 %in% c("FTLD-TDP","ALS-TDP")) %>%
mutate(call = fct_relevel(call,
"C/C", "C/G", "G/G")) %>%
mutate(stmn2_psi_groups = as.numeric(cut_interval(log10(stmn_2_cryptic_psi), n = 2))) %>%
mutate(stmn2_psi_groups = ifelse(is.na(stmn2_psi_groups)," No STMN2",stmn2_psi_groups)) %>%
mutate(stmn2_psi_groups = case_when(stmn2_psi_groups == 1 ~ " Low STMN2",
stmn2_psi_groups == 2 ~ "High STMN2",
TRUE ~ stmn2_psi_groups)) %>%
filter(unc13a_cryptic_leaf_psi > 0 ) %>%
ggbarplot( x = "call",
y = "cryptic_psi",
color = 'call',
facet.by = 'stmn2_psi_groups',
position = position_dodge(0.8),
add = c("mean_se","jitter")) +
ylab("UNC13A CE PSI") +
scale_color_manual(values = c("#88CCEE","#44AA99","#105e2a")) +
theme(text = element_text(size = 20)) +
stat_compare_means(aes(group = call), label = "p.format") +
stat_compare_means(aes(group = call),comparisons = genotype_comparisons,label = "p.signif") +
xlab("")
Variant allele expression in TDP-43 KD experiments
Across our KD’s, we observed a clear allelic bias in the SK-DZ-N cells
kd_vafs = fread(file.path(here::here(),"data","all_kds_unc13.snp.out.tsv"))
kd_vafs %>%
left_join(rel_rna_cryptic_amount) %>%
filter(!is.na(condition)) %>%
mutate(VAF = alt_reads / total) %>%
filter(condition != "control" ) %>%
filter(alt_reads > 5) %>%
ggplot(aes(x = stmn_2_cryptic_psi,y = cryptic_psi, fill = -(VAF - 0.5),size = total)) +
geom_point(pch = 21) +
coord_fixed() +
ylim(0,0.8) +
xlim(0,0.8) +
geom_abline() +
scale_fill_gradient2()Joining, by = "sample"
Liu Facs Neurons
Also look at the coverage in the Liu nuclear facs sorted neurons
liu_vafs = fread(file.path(here::here(),"data","liu_facs_neurons_unc13.snp.out.tsv"))
liu_stmn2 = fread(file.path(here::here(),"data","liu_stmn2_and_unc13aaggregated.clean.annotated.bed"))
liu_stmn2[,sample := gsub(".SJ.out","",V4)]
liu_stmn2 = liu_stmn2 %>%
unique() %>%
pivot_wider(id_cols = 'sample',
names_from = "V7",
values_from = 'V5',
values_fill = 0)
liu_stmn2 <- liu_stmn2 %>%
mutate(stmn2_psi = STMN2_cryptic / (STMN2_cryptic + STMN2_annotated)) %>%
mutate(unc13a_psi = (UNC13A_3prime + UNC13A_5prime + UNC13A_5prime_2)/ (UNC13A_annotated + UNC13A_3prime + UNC13A_5prime + UNC13A_5prime_2))
liu_vafs %>%
mutate(VAF = alt_reads / total) %>%
data.table::melt(id.vars = c("sample",'VAF','total')) %>%
mutate(allele = ifelse(variable == "ref_reads", "C","G")) %>%
mutate(sample_name = sub("_TDP_.*", "", sample)) %>%
mutate(sample_name = gsub("_unsorted", "", sample_name)) %>%
mutate(condition = sub(".*_", "", sample)) %>%
ggplot(aes(x = condition,y = value, fill = allele)) +
geom_col(pch = 21, size = 4) +
facet_wrap(~sample_name,scales = "free") 
liu_stmn2 %>%
dplyr::select(stmn2_psi,sample,unc13a_psi) %>%
data.table::melt() %>%
mutate(sample_name = sub("_TDP_.*", "", sample)) %>%
filter(!grepl("unsorted",sample_name)) %>%
mutate(condition = sub(".*_", "", sample)) %>%
mutate(condition = ifelse(grepl("negative",condition), "TDP-43 \nNegative", "TDP-43 \nPositive")) %>%
mutate(variable = ifelse(grepl("stmn2",variable), "STMN2", "UNC13A")) %>%
ggplot(aes(x = condition,y = value, fill = variable)) +
geom_col(size = 4,position = 'dodge') +
facet_wrap(~sample_name,scales = "free_x") +
scale_fill_manual(values = c("#004D40","#882255")) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(text = element_text(size = 24)) +
xlab("") +
ylab("PSI") +
theme(legend.position = 'top') +
theme(legend.title=element_blank()) +
stat_compare_means()Using sample as id variables
liu_stmn2 %>%
mutate(stmn2_psi = STMN2_cryptic / (STMN2_cryptic + STMN2_annotated)) %>%
mutate(unc13a_psi = (UNC13A_3prime + UNC13A_5prime)/ (UNC13A_annotated + UNC13A_3prime + UNC13A_5prime)) %>%
left_join(liu_vafs) %>%
mutate(alt_vaf = (alt_reads)/total) %>%
mutate(condition = ifelse(grepl("negative",sample),"negative", "positive")) %>%
filter(!grepl("unsorted",sample)) %>%
ggplot(aes(y = unc13a_psi,x = stmn2_psi, fill = alt_vaf)) +
geom_point(pch = 21,size = 5) +
ggtitle("STMN2 PSI and the VAF of the G allele \n in the Liu Nuclei ") +
facet_grid(~condition) +
scale_fill_viridis_c(option = "plasma") +
coord_fixed() +
geom_abline()Joining, by = "sample"
Variant allele expression in patients
nycg_vafs = fread(file.path(here::here(),"data","NYGC_all_samples_UNC13A_snp_coverage.tsv"),fill = T)
nycg_vafs %>%
left_join(clean_data_table) %>%
filter(call == "C/G") %>%
mutate(fraction_risk = alt_reads / total) %>%
filter(!is.na(fraction_risk)) %>%
filter(stmn_2_cryptic_psi_leaf > 0 ) %>%
filter(unc13a_cryptic_leaf_psi > 0 ) %>%
ggplot(aes(x = stmn_2_cryptic_psi_leaf, y = unc13a_cryptic_leaf_psi)) +
geom_point(size = 4, pch = 21, aes(fill = fraction_risk - 0.5)) +
xlab("STMN2 Cryptic PSI") +
ylab("UNC13A CE PSI") +
geom_abline() +
scale_fill_gradient2()Joining, by = "sample"
nycg_vafs %>%
left_join(clean_data_table) %>%
filter(call == "C/G") %>%
mutate(fraction_risk = alt_reads / total) %>%
filter(!is.na(fraction_risk)) %>%
filter(stmn_2_cryptic_psi_leaf > 0 ) %>%
filter(unc13a_cryptic_leaf_psi > 0 ) %>%
mutate(stmn2_unc_fract = ((unc13a_cryptic_leaf_psi * 100) / (stmn_2_cryptic_psi_leaf * 100))) %>%
ggplot(aes(x = stmn2_unc_fract, y = fraction_risk)) +
geom_point(size = 4, pch = 21, aes(fill = fraction_risk - 0.5)) +
xlab("STMN2 Cryptic PSI") +
ylab("UNC13A CE PSI") +
scale_fill_gradient2()Joining, by = "sample"